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NLP

Guide to word vectors with gensim and keras

  Today, I tell you what word vectors are, how you create them in python and finally how you can use them with neural networks in keras. For a long time, NLP methods use a vectorspace model to represent words…. Continue Reading →

A strong baseline to classify toxic comments on Wikipedia with fasttext in keras

This time we’re going to discuss a current machine learning competion on kaggle. In this competition, you’re challenged to build a model that’s capable of detecting different types of toxicity in comments from Wikipedia’s talk page edits. I will show you how to create a strong baseline using python and keras.

Sequence tagging with a LSTM-CRF

This is the fourth post in my series about named entity recognition. The last time we used a recurrent neural network to model the sequence structure of our sentences. Now we use a hybrid approach combining a bidirectional LSTM model and a CRF model. The so called LSTM-CRF is a state-of-the-art approach to named entity recognition.

Guide to sequence tagging with neural networks in python

Named entity recognition series: Introduction To Named Entity Recognition In Python Named Entity Recognition With Conditional Random Fields In Python Guide To Sequence Tagging With Neural Networks In Python Sequence Tagging With A LSTM-CRF Enhancing LSTMs With Character Embeddings For… Continue Reading →

Named entity recognition with conditional random fields in python

This is the second post in my series about named entity recognition. This time, we’re going to look into a more sophisticated algorithm, a so called conditional random field.

Introduction to named entity recognition in python

In this post you will learn how to do basic Named Entity Recognition (NER) in python. This is the first post in a series about NER.

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